At a Glance

Name: Discover sentiments in customer service tweets
Price: Free CAD
Rating: 4.7 (15 reviews)

Dive into sentiment analysis using natural language processing (NLP) techniques. Classify tweets by leveraging VADER, XGBoost, and logistic regression algorithms and uncover insights from textual data, offering valuable perspectives on sentiment trends. Learn how to create engaging visualizations like word clouds and bar charts to enhance your understanding of sentiment analysis results.

In this guided project, you'll explore the tools and techniques necessary for doing sentiment analysis. Utilizing NLP methods, you'll preprocess data and employ the VADER sentiment analysis tool to train your model on a diverse set of Twitter customer service tweets. Enhance model accuracy through hyperparameter tuning, and leverage insights gained from VADER to apply XGBoost and Logistic Regression models, categorizing emotional tones of tweets into negative, neutral, or positive sentiments.

A Look at the Project Ahead

In this guided project, you will:

Get hands-on experience with sentiment analysis
Learn to preprocess the data with natural language processing tasks
Get hands-on experience with evaluating your model with VADER, XGBoost, and logistic regression algorithms
Visualize the insights with bar charts and word clouds

What You'll Need

No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
Some understanding of Python: Having some understanding of Python is required for some preprocessing text tasks.
Some understanding of statistical concepts: It's helpful to have some understanding of statistic concepts, particularly XGBoost and Logistic Regression algorithms.

Offered By: IBMSkillsNetwork

Discover sentiments in customer service tweets

At a Glance

A Look at the Project Ahead

What You'll Need